Personal science is the practice of addressing personally relevant health questions through self-research. Implementing personal science can be challenging, owing to the need to develop and adopt research protocols, tools and methods. While online communities can provide valuable peer support, tools for systematically accessing community knowledge are lacking. The objective of this study is to apply a participatory design process involving a community of personal science practitioners to develop a peer-produced knowledge base that supports the needs of practitioners as consumers and contributors of knowledge. The process led to the development of the Personal Science Wiki, an open repository for documenting and accessing individual self-tracking projects while facilitating the establishment of consensus knowledge. After initial design iterations and a field testing phase, we performed a user study with 21 participants to test and improve the platform, and to explore suitable information architectures. The study deepened our understanding of barriers to scaling the personal science community, established an infrastructure for knowledge management actively used by the community and provided lessons on challenges, information needs, representations and architectures to support individuals with their personal health inquiries.

BACKGROUND:Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.OBJECTIVE:This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.METHODS:We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.RESULTS:The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.CONCLUSIONS:As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

Background: Wearables have been used widely for monitoring health in general and recent research results show that they can be used for predicting infections based on physiological symptoms. So far the evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are comprised of people interested in learning about themselves individually using their own data, often gathered via wearable devices. Objective: We explore how a co-creation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system to monitor symptoms of infection alongside wearable sensor data. Methods: We engaged into a co-creation and design process with an existing community of personal science practitioners, jointly developing a working prototype of an online tool to perform symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis, investigating the process of how this prototype was created in a decentralized and iterative fashion. Results: The Quantified Flu prototype allows users to perform daily symptom reporting and is capable of visualizing those symptom reports on a timeline together with the resting heart rate, body temperature and respiratory rate as measured by wearable devices. We observe a high level of engagement, with over half of the 92 users that engaged in the symptom tracking becoming regular users, reporting over three months of data each. Furthermore, our netnographic analysis highlights how the current Quantified Flu prototype is a result of an interactive and continuous co-creation process in which new prototype releases spark further discussions of features and vice versa. Conclusions: As shown by the high level of user engagement and iterative development, an open co-creation process can be successfully used to develop a tool that is tailored to individual needs, decreasing dropout rates.

This paper introduces a conceptual framework to guide research and education into the practice of personal science, which we define as using empirical methods to pursue personal health questions. Personal science consists of five activities: questioning, designing, observing, reasoning, and discovering. These activities are conceptual abstractions derived from review of self-tracking practices in the Quantified Self community. These practices have been enabled by digital tools to collect personal real-world data. Similarities and differences between personal science, citizen science and single subject (N-of-1) research in medicine are described. Finally, barriers that constrain widespread adoption of personal science and limit the potential benefits to individual wellbeing and clinical and public health discovery are briefly discussed, with perspectives for overcoming these barriers.

Cardiovascular disease risk assessment relies on single time-point measurement of risk factors. Although significant daily rhythmicity of some risk factors (e.g., blood pressure and blood glucose) suggests that carefully timed samples or biomarker timeseries could improve risk assessment, such rhythmicity in lipid risk factors is not well understood in free-living humans. As recent advances in at-home blood testing permit lipid data to be frequently and reliably self-collected during daily life, we hypothesized that total cholesterol, HDL-cholesterol or triglycerides would show significant time-of-day variability under everyday conditions. To address this hypothesis, we worked with data collected by 20 self-trackers during personal projects. The dataset consisted of 1,319 samples of total cholesterol, HDL-cholesterol and triglycerides, and comprised timeseries illustrating intra and inter-day variability. All individuals crossed at least one risk category in at least one output within a single day. 90% of fasted individuals (n = 12) crossed at least one risk category in one output during the morning hours alone (06:00–08:00) across days. Both individuals and the aggregated group show significant, rhythmic change by time of day in total cholesterol and triglycerides, but not HDL-cholesterol. Two individuals collected additional data sufficient to illustrate ultradian (hourly) fluctuation in triglycerides, and total cholesterol fluctuation across the menstrual cycle. Short-term variability of sufficient amplitude to affect diagnosis appears common. We conclude that cardiovascular risk assessment may be augmented via further research into the temporal dynamics of lipids. Some variability can be accounted for by a daily rhythm, but ultradian and menstrual rhythms likely contribute additional variance.

Objectives Participant-led research (PLR) is a rapidly developing form of citizen science in which individuals can create personal and generalisable knowledge. Although PLR lacks a formal framework for ethical review, participants should not be excused from considering the ethical implications of their work. Therefore, a PLR cohort consisting of 24 self-trackers aimed to: (1) substitute research ethics board procedures with engagement in ethical reflection before and throughout the study and (2) draft principles to encourage further development of the governance and ethical review of PLR. Methods A qualitative case study method was used to analyse the ethical reflection process. Participants discussed study risks, risk management strategies and benefits pre-project, during a series of weekly webinars, via individual meetings with the participant-organisers, and during semi-structured interviews at project completion. Themes arising from discussions and interviews were used to draft prospective principles to guide PLR. Results Data control, aggregation and identifiability were the most common risks identified. These were addressed by a commitment to transparency among all participants and by establishing participant control via self-collection and self-management of data. Group discussions and resources (eg, assistance with experimental design and data analysis) were the most commonly referenced benefits of participation. Additional benefits included greater understanding of one’s physiology and greater ability to structure an experiment. Nine principles were constructed to encourage further development of ethical PLR practices. All participants expressed interest in participating in future PLR. Conclusions Projects involving a small number of participants can sustain engagement in ethical reflection among participants and participant-organisers. PLR that prioritises transparency, participant control of data and ongoing risk-to-benefit evaluation is compatible with the principles that underlie traditional ethical review of health research, while being appropriate for a context in which citizen scientists play the central role.

Single subject research design, also known as N-of-1 research, is a scientific method in which an individual person serves as the research subject. We treat “N-of-1” and “single subject” as synonyms encompassing all scientific practice which focuses on observations made about a single person. Other names for similar and overlapping approaches include: single case experiments [1–3] single case research [4, 5], single case designs [6], and single patient trials [7]. Some authors distinguish between single subject research in general, which may be descriptive and exploratory in character, and single subject experiments that are prospectively planned and use formal methods such as randomization, blinding, or crossover comparisons. Here, we use N-of-1 and single subject research as synonymous, high level general terms for research focused on an individual rather than a group. N-of-1 research is common in applied fields of psychology, education, and human behavior where it has benefited from extensive methodical research and practical guidance for practitioners [8, 9]. However, over a half-century of study and advocacy, including pioneering publications by Guyatt et al., Larson et al., Mahon et al., and others, have failed to establish single subject science as central to research and practice in medicine [10–13]. A systematic review of 122 eligible N-of-1 studies published between 1985 and 2013 showed wide variation in methodology and reporting, reducing the power of these studies to influence practice [14]. Researchers advocating N-of-1 techniques have noted that the practical obstacles to design, conduct, analyze and apply the results for single subjects have simply been too high [15, 16]. Nevertheless the rise of personalized medicine and patient-centered research create new opportunities for using N-of-1 methods [17, 18]. Recent key publications include an extensive and comprehensive user guide for the design and implementation of N-of-1 trials [19], an update of the standard (CONSORT) for reporting N-of-1 trials [20, 21], and a special issue of the Journal of Clinical Epidemiology devoted to individual patients as the primary source and target of clinical research [22]. General public interest in gathering data about health is also growing. A Pew Internet study conducted in 2013 found that 1 in 5 Americans use some form of technology to track their health [23]. In 2016, the number of consumers in the United States who use mobile health apps increased from 16 percent in 2014 to 33 percent and the number of consumers who use health wearables increased from 9 percent to 21 percent [24]. According to data from the International Data Corporation (IDC), 104.3 million wearable devices were shipped in 2016, a number that is likely to be almost doubled by 2021 [25]. The increasing availability of home blood testing kits, wearable glucose monitors, and heart rate monitors, among other consumer health tools and services, suggest a large scale transformation of the measurement context for N-of-1 research. The combination of increased public interest and reliable measurement technologies broadly available may reduce the barriers to application of N-of-1 methodology [16, 26]. These consumer technologies have already attracted research attention. For instance, activity trackers made by Fitbit, Inc, have been deployed as instrumentation in over 450 public scientific studies [27]. Of course, application of wearables for clinical or research practice requires the technology to be valid and reliable. Research has found considerable variation of accuracy in different consumer wearables, including activity trackers [28–30], sleep trackers [31, 32], and wrist worn heart rate monitors [33, 34]. Despite this variation, there have been some notable successes. For instance, in an innovative two year study published in 2017, Li et al. demonstrated that measurement of heart rate and skin temperature using consumer wearables could predict inflammatory response as revealed by laboratory blood work showing elevated hs-CRP and onset of symptoms [35]. In presenting the articles in this focus theme, we aim to encourage attention to single subject research from from both scholars and researchers in health and biomedical informatics who may play a key role in advancing its practical methods and resolving doubts about its power and validity.

From the Publisher:"Louis Rossetto had no money, no home, no job. Five years later he owned the hottest magazine in America and was poised to become an international tycoon, with America's most powerful financiers by his side." "Rossetto was the founder and editor of Wired, whose hyperactive Day-Glo pages proclaimed that every American institution was obsolete. Instantly, Wired was everywhere - on television, passed around the halls of Congress, displayed in the office of the president of the United States. Wired's headquarters in San Francisco became a pilgrimage site for everybody who wanted to be at the white-hot center of the digital revolution. Not since the early days of Jann Wenner and Rolling Stone had anybody so brilliantly channeled the enthusiasms of his era." But this was only the beginning. Wired cast an uncanny spell, creating a feedback loop that grew stunningly out of control. Wired's online site, HotWired, designed and sold the first banner advertisements for the World Wide Web, unleashing a commercial frenzy. Wired reached for empire, with a book-publishing company, a broadcast division, and foreign editions all over the globe. But as the market's enthusiasm outstripped the limits of reason, Rossetto faced a battle over the fate of Wired that would prove the ultimate test of his radical ideas.